Machine Learning for Diagnosis of Occlusive MI in LBBB Patients (AI-LBBB)
Development of a Machine Learning Model for the Diagnosis of Occlusive Myocardial Infarction in the Setting of Left Bundle Branch Block
This study investigates a new way to diagnose severe heart attacks in patients who have a specific electrical heart pattern called a Left Bundle Branch Block (LBBB). When patients present to the emergency department with chest pain, doctors routinely perform an electrocardiogram (ECG) to check for a heart attack. However, the presence of an LBBB can alter the heart's electrical signals on the ECG, effectively masking or hiding the typical signs of an ongoing acute coronary occlusion (a completely blocked artery). This making it highly challenging for emergency physicians to make an accurate and rapid diagnosis.
The primary purpose of this prospective and observational research is to develop and evaluate an artificial intelligence/machine learning (ML) model that can analyze digital 12-lead ECG signals to accurately predict a true blocked coronary artery in patients with LBBB. The machine learning model will analyze raw digital ECG waveforms to detect subtle, microscopic patterns that might be missed by the human eye.
To confirm the accuracy of the model, its predictions will be compared directly with invasive coronary angiography results, which is the gold standard reference method used to visualize blocked vessels. Additionally, the study aims to evaluate if the model can differentiate between a true heart attack caused by a blocked artery (Type 1 MI) and other non-occlusive conditions that cause elevated heart enzymes (Type 2 MI). Ultimately, the investigators intend to determine whether integrating this machine learning tool into emergency care can safely reduce the rate of unnecessary emergency invasive procedures for patients who do not have a true coronary blockage.
Panoramica dello studio
Stato
Condizioni
Intervento / Trattamento
Tipo di studio
Iscrizione (Stimato)
Contatti e Sedi
Luoghi di studio
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Karatay
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Konya, Karatay, Turchia (Türkiye), 42100
- Reclutamento
- Konya City Hospital
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Contatto:
- Ahmet Gumus, MD, Emergency Medicine Residen
- Numero di telefono: +905547957490
- Email: ahmetgms88@gmail.com
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Criteri di partecipazione
Criteri di ammissibilità
Età idonea allo studio
- Adulto
- Adulto più anziano
Accetta volontari sani
Metodo di campionamento
Popolazione di studio
Descrizione
Inclusion Criteria:
- Patients aged 18 years and older who present to the emergency department. Patients presenting with acute ischemic chest pain or clinical ischemia-equivalent symptoms (such as acute dyspnea, unexplained diaphoresis, or syncope).
Patients with a confirmed Left Bundle Branch Block (LBBB) on their initial 12-lead electrocardiogram (ECG), which can be either newly developed or known/chronic.
Patients who undergo invasive coronary angiography during their index hospital admission.
Patients or their legally authorized representatives who provide written informed consent to participate in the study.
Exclusion Criteria:
- Patients under the age of 18. Pregnant or lactating women. Patients with poor-quality or uninterpretable digital ECG recordings due to severe artifact, missing leads, or technical errors.
Patients who develop cardiopulmonary arrest before an initial diagnostic 12-lead ECG can be obtained in the emergency department.
Patients transferred from another healthcare facility who have already undergone coronary angiography or revascularization.
Patients who decline to participate or refuse to provide written informed consent.
Piano di studio
Come è strutturato lo studio?
Dettagli di progettazione
Cosa sta misurando lo studio?
Misure di risultato primarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
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Diagnostic Performance for Occlusive Acute Myocardial Infarction
Lasso di tempo: Within the emergency department index visit (typically within 24 hours of presentation).
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Evaluation of the developed machine learning model's diagnostic performance in predicting angiographically proven acute coronary occlusion (defined as TIMI 0-1 flow or equivalent true occlusion during catheterization).
The primary metrics to evaluate this outcome will include the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC), Sensitivity, Specificity, Positive Predictive Value (PPV), and Negative Predictive Value (NPV).
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Within the emergency department index visit (typically within 24 hours of presentation).
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Misure di risultato secondarie
Misura del risultato |
Misura Descrizione |
Lasso di tempo |
|---|---|---|
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Title: Differentiation Performance Between Type 1 MI and Type 2 MI
Lasso di tempo: Within the hospital stay (up to 7 days).
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Evaluation of the machine learning model's performance (measured by AUC, sensitivity, and specificity) to distinguish between acute coronary occlusion (Type 1 MI) and non-occlusive ischemic myocardial injury or supply-demand mismatch presenting with elevated cardiac troponin (Type 2 MI).
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Within the hospital stay (up to 7 days).
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Projected Reduction Rate of Unnecessary Angiographies
Lasso di tempo: Calculated at the study completion
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Simulation and post-hoc analysis to quantify the potential relative reduction in the rate of emergency invasive coronary angiographies among LBBB patients without true coronary occlusion by applying the model's diagnostic probability scores.
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Calculated at the study completion
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Collaboratori e investigatori
Sponsor
Studiare le date dei record
Studia le date principali
Inizio studio (Stimato)
Completamento primario (Stimato)
Completamento dello studio (Stimato)
Date di iscrizione allo studio
Primo inviato
Primo inviato che soddisfa i criteri di controllo qualità
Primo Inserito (Effettivo)
Aggiornamenti dei record di studio
Ultimo aggiornamento pubblicato (Effettivo)
Ultimo aggiornamento inviato che soddisfa i criteri QC
Ultimo verificato
Maggiori informazioni
Termini relativi a questo studio
Termini MeSH pertinenti aggiuntivi
- Malattia del sistema di conduzione cardiaca
- Dolore
- Manifestazioni neurologiche
- Malattie vascolari
- Malattia cardiovascolare
- Processi patologici
- Malattie cardiache
- Aritmie, cardiache
- Infarto
- Necrosi
- Arresto cardiaco
- Embolia e Trombosi
- Malattia coronarica
- Ischemia miocardica
- Ischemia
- Condizioni patologiche, segni e sintomi
- Segni e sintomi
- Trombosi
- Infarto miocardico
- Blocco di branca-branco
- Dolore al petto
- Occlusione coronarica
Altri numeri di identificazione dello studio
- 2026/133
Informazioni su farmaci e dispositivi, documenti di studio
Studia un prodotto farmaceutico regolamentato dalla FDA degli Stati Uniti
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